What is AI ( Artificial intelligence) ?

AI (Artificial Intelligence)

                                                                                         Image Credit:-towardsdatascience.com

In computer science, Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in 

contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of 

"intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its 

goals.

1. More in detail, Kaplan, and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and 

to use those learnings to achieve specific goals and tasks through flexible adaptation

2. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other 

human minds, such as "learning" and "problem-solving

3. The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed 

from the definition, a phenomenon is known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet.

4. For instance, optical character recognition is frequently excluded from "artificial intelligence", has become a routine technology

5. Modern machine capabilities generally classified as AI include successfully understanding human speech

6. competing at the highest level in strategic game systems (such as chess and Go)

7. autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: 

analytical, human-inspired, and humanized artificial intelligence

Examples of AI

AI covers quite a vast area and so it's no surprise there are numerous examples of how it's being used in both industry and everyday life.

For example, driverless cars employ AI to make decisions about how a car should react in certain scenarios, such as an obstacle getting 

in the way. Should the car instantly stop, just slow down or keep going? Naturally, in reality, this is dependent on what the car has 

sensed is in the way.

On a more controversial level, it's also being developed to make much harder decisions, where the loss of life can be minimized, but not 

avoided, by choosing who to kill when there's certainly someone will have to die in order for others to survive.

Less alarming real-world examples of AI include using the technology to test humans on a strategic level. For example, Google's AlphaGo 

the robot was used to prove that computers can be smarter than humans by beating some of the world's leading Chinese board game Go 

players.

To a lesser extent, many of the voice assistant systems including Siri, Google Now and Cortana use AI to make decisions based upon your 

commands. For example, if you ask Siri for a restaurant recommendation, it will use the data it knows about you (such as where you are, 

what kind of food you like) to recommend a restaurant to you.

Weak AI vs strong AI

We encounter the simplest form of AI in everyday consumer products. Known as 'weak AI', these machines are designed to be extremely 

intelligent at performing a certain task. An example of this is Apple's Siri, which is designed to appear very intelligent but actually uses 

the internet as its information source. The virtual assistant can participate in conversations but is limited to doing so in a restrictive, 

the predefined manner that can lead to inaccurate results.

On the other hand, in its most complex form, AI may theoretically have all the cognitive functions a human possesses, such as the ability 

to learn, predict, reason and perceive situational cues. This 'strong AI' can be perceived as the ultimate goal, but humans have yet to 

create anything deemed to be a fully independent AI.

Currently, the most compelling work is situated in the middle of these two types of AI. The idea is to use human reasoning as a guide, but 

not necessarily replicate it entirely. For example, IBM's supercomputer Watson can sift through thousands of datasets to make 

evidence-based conclusions.

Implementing Machine Learning in your Business

Machine learning is often used to predict future outcomes based on historical data. For example, organizations use machine learning to 

predict how many of their products will be sold in future fiscal quarters based on a particular demographic; or estimate which customer 

the profile has the highest probability to become dissatisfied or the most loyal to your brand. Such predictions allow better business 

decisions, more personal user experience, and the potential to reduce customer retention costs. Complementary to Business Intelligence 

(BI), which focuses on reporting past business data, ML predicts future outcomes based on past trends and transactions.

There are several steps that comprise a successful implementation of ML in a business. First, identifying the right problem -- identifying 

the prediction that would benefit the business if ascertained. Next, the data must be collected, based on historical business metrics 

(transactions, sales, attrition, etc.). Once the data is aggregated, an ML model can be built based on that data. The ML model is run and 

the prediction output of the model is applied back to the business system to make more informed decisions.